Discrimination of white teas produced from fresh leaves with different maturity by near-infrared spectroscopy

Explorative study for the rapid detection of adulterated surimi using gas chromatography-ion mobility spectrometry

Driven by economic interests, surimi adulteration has become a high-frequency issue. This study aims to assess the feasibility of gas chromatography-ion mobility spectrometry (GC-IMS) in detecting surimi adulteration. In this work, three common adulterated surimi models were established by mixing with different fish species and ratios. The fingerprints enabled a clear discrimination among different tuna surimi, and other two surimi models with different mixing ratios also showed VOCs (volatile organic compounds) differences. Results of unsupervised principal component analysis (PCA) and supervised partial least-squares discrimination analysis (PLS-DA) revealed that different types of adulterated surimi models can be well separated from each other. A total of 12, 16, and 9 VOCs were selected as the potential markers in three simulated models by PLS-DA method, respectively. Therefore, GC-IMS coupled with certain chemometrics is expected to serve as an alternative analytical tool to directly and visually detect adulterated surimi.

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

Chemometrics-based investigation of non-volatiles/volatiles flavor of tencha (Camellia sinensis cv. Yabukita, Longjing 43 and Baiye 1)

The increasing demand for tea consumption calls for the development of more products with distinct characteristics. The sensory quality of tencha is significantly determined by innate differences among tea cultivars. However, the correlations between the chemical composition and sensory traits of tencha are still unclear. To enhance the understanding of the flavor formation mechanism in tencha and further to develop new cultivars resources, we investigated non-volatiles and volatile metabolites as well as sensory traits in tencha from different tea cultivars (Camellia sinensis cv. Yabukita, Longjing 43 and Baiye 1); the relationships between the flavor traits and non-volatiles/volatiles were further evaluated by partial least squares - discriminate analysis (PLS-DA), multiple factor analysis (MFA) and multidimensional alignment (MDA) analysis. A total of 64 non-volatiles and 116 volatiles were detected in all samples, among which 71 metabolites were identified as key flavor-chemical contributors involving amino acids, flavonol glycosides, flavones, catechins, ketones, alcohols, hydrocarbons, aldehydes, esters and acids. The levels of taste-related amino acids, flavonol glycosides and gallic acid varied significantly among the tencha samples made from different tea cultivars. All the samples exhibited typical quality characteristics of tencha. The tencha from Camellia sinensis cv. Longjing 43 and Camellia sinensis cv. Baiye 1 (cultivated in the open) exhibited higher levels of amino acids and gallic acid, which were associated with the umami taste and mellow taste of tea infusion. Abundant flavonol glycosides were related to the astringency, while partial tri-glycosides specifically quercetin-3-O-galactoside-rhamnoside-glucoside and total of flavonol galactoside-rhamnoside-glucoside were associated with mellow taste. The floral alcohols were identified as significant contributors to the refreshing aroma traits of tencha. The green, almond-like, acidic and fruity odorants were associated with a green and fresh aroma, while the green, cheesy and waxy odorants such as ketones, esters, acids and hydrocarbons were associated with seaweed-like aroma. This study provides insight into sensory-related chemical profiles of tencha from different tea cultivars, supplying valuable information on flavor and quality identification for tencha.

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.

A Vis/NIR spectra-based approach for identifying bananas infected with Colletotrichum musae

Introduction

Anthracnose of banana caused by Colletotrichum species is one of the most serious post-harvest diseases, which can cause significant yield losses. Clarifying the infection mechanism of the fungi using non-destructive methods is crucial for timely discriminating infected bananas and taking preventive and control measures.

Methods

This study presented an approach for tracking growth and identifying different infection stages of the C. musae in bananas using Vis/NIR spectroscopy. A total of 330 banana reflectance spectra were collected over ten consecutive days after inoculation, with a sampling rate of 24 h. The four-class and five-class discriminant patterns were designed to examine the capability of NIR spectra in discriminating bananas infected at different levels (control, acceptable, moldy, and highly moldy), and different time at early stage (control and days 1-4). Three traditional feature extraction methods, i.e. PC loading coefficient (PCA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), combining with two machine learning methods, i.e. partial least squares discriminant analysis (PLSDA) and support vector machine (SVM), were employed to build discriminant models. One-dimensional convolutional neural network (1D-CNN) without manually extracted feature parameters was also introduced for comparison.

Results

The PCA-SVM and·SPA-SVM models had good performance with identification accuracies of 93.98% and 91.57%, 94.47% and 89.47% in validation sets for the four- and five-class patterns, respectively. While the 1D-CNN models performed the best, achieving an accuracy of 95.18% and 97.37% for identifying infected bananas at different levels and time, respectively.

Discussion

These results indicate the feasibility of identifying banana fruit infected with C. musae using Vis/NIR spectra, and the resolution can be accurate to one day.

A Study on Origin Traceability of White Tea (White Peony) Based on Near-Infrared Spectroscopy and Machine Learning Algorithms

Identifying the geographical origins of white tea is of significance because the quality and price of white tea from different production areas vary largely from different growing environment and climatic conditions. In this study, we used near-infrared spectroscopy (NIRS) with white tea (n = 579) to produce models to discriminate these origins under different conditions. Continuous wavelet transform (CWT), min-max normalization (Minmax), multiplicative scattering correction (MSC) and standard normal variables (SNV) were used to preprocess the original spectra (OS). The approaches of principal component analysis (PCA), linear discriminant analysis (LDA) and successive projection algorithm (SPA) were used for features extraction. Subsequently, identification models of white tea from different provinces of China (DPC), different districts of Fujian Province (DDFP) and authenticity of Fuding white tea (AFWT) were established by K-nearest neighbors (KNN), random forest (RF) and support vector machine (SVM) algorithms. Among the established models, DPC-CWT-LDA-KNN, DDFP-OS-LDA-KNN and AFWT-OS-LDA-KNN have the best performances, with recognition accuracies of 88.97%, 93.88% and 97.96%, respectively; the area under curve (AUC) values were 0.85, 0.93 and 0.98, respectively. The research revealed that NIRS with machine learning algorithms can be an effective tool for the geographical origin traceability of white tea.

Online quantitative substrate, product, and cell concentration in citric acid fermentation using near-infrared spectroscopy combined with chemometrics

As a mature platform compound, citric acid (CA) is mainly produced by Aspergillus niger (A. niger) through submerged fermentation. However, the CA fermentation process is still regulated based on experience and limited offline data, so real-time monitoring and intelligent precise control of the fermentation process cannot be carried out. In this study, near-infrared (NIR) spectroscopy combined with different chemometrics methods was used to quantify the substrate, product, and cell concentration of CA fermentation online. The predictive performance of total sugar (TS), CA, and dry cell weight (DCW) concentrations were compared between traditional partial least squares (PLS) and intelligent stacked auto-encoder (SAE) modeling methods. Theresults showed that both PLS and SAE models had good performance in predicting TS and CA. The performance, accuracy, and precision of the PLS models are slightly better than those of the SAE models in predicting TS and CA. SAE model was superior to the PLS model in predicting DCW concentration. The SAE modeling method has advantages in predicting the concentration of complex components. In this study, the multi-parameter online prediction was realized in the complex system of CA fermentation, which provided the basis for real-time intelligent control of the fermentation process.

Rapid and accurate determination of diesel multiple properties through NIR data analysis assisted by machine learning

The rapid and accurate detection of diesel multiple properties is an important research topic in petrochemical industry that is conducive to diesel quality assessment and environmental pollution mitigation. To that end, this paper developed a new machine learning model for near infrared (NIR) spectroscopy capable of simultaneously determining diesel density, viscosity, freezing point, boiling point, cetane number and total aromatics. The model combined improved XY co-occurrence distance (ISPXY) and differential evolution-gray wolf optimization support vector machine (DEGWO-SVM) to attain the goal of rapidity and accuracy. Experimental results indicated that the average recovery, mean square error, mean absolute percentage error and determination coefficient of the presented method outperformed those of the existing machine learning methods. The proposed hybrid model provides superior solution to the problem of low efficiency and high cost of diesel quality detection, and has the potential to be utilized as a promising tool for diesel routine monitoring.

L-theanine exuded from Camellia sinensis roots regulates element cycling in soil by shaping the rhizosphere microbiome assembly

Root exudate metabolites are a key medium for the interaction between plants and soil microbiota. L-theanine is a unique non-protein amino acid critical for the flavor and potential health benefits of tea products; however, its biological function in tea plants is not well understood. As L-theanine is mainly synthesized in the roots of tea plants, we hypothesized that L-theanine could affect the function of the rhizosphere microbiota by modulating microbial assembly. In the present study, L-theanine was detected in the exudates of tea plant roots using liquid chromatography-mass spectrometry. Additionally, 16S rRNA gene sequencing revealed that L-theanine significantly altered the structure of the rhizosphere microbiota and selectively shaped rhizosphere microbial assembly. Moreover, metagenomic data showed that L-theanine affected the abundance of genes encoding element cycling in soil. Interestingly, the denitrification and complete nitrification pathways were significantly inhibited by L-theanine by decreasing the narH, napA, and napB genes abundance. These findings provide new insights into the biological function of L-theanine, as well as the implications of interactions between tea plant root exudates and the rhizosphere microbiome.

Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm⁻¹ using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.

Effect of Yellowing Duration on the Chemical Profile of Yellow Tea and the Associations with Sensory Traits

The yellowing process is the crucial step to form the characteristic sensory and chemical properties of yellow tea. To investigate the chemical changes and the associations with sensory traits during yellowing, yellow teas with different yellowing times (0–13 h) were prepared for sensory evaluation and chemical analysis. The intensities of umami and green-tea aroma were reduced whereas sweet taste, mellow taste and sweet aroma were increased under long-term yellowing treatment. A total of 230 chemical constituents were determined, among which 25 non-volatiles and 42 volatiles were the key chemical contributors to sensory traits based on orthogonal partial least squares discrimination analysis (OPLS-DA), multiple factor analysis (MFA) and multidimensional alignment (MDA) analysis. The decrease in catechins, flavonol glycosides and caffeine and the increase in certain amino acids contributed to the elevated sweet taste and mellow taste. The sweet, woody and herbal odorants and the fermented and fatty odorants were the key contributors to the characteristic sensory feature of yellow tea with sweet aroma and over-oxidation aroma, including 7 ketones, 5 alcohols, 1 aldehyde, 5 acids, 4 esters, 5 hydrocarbons, 1 phenolic compound and 1 sulfocompound. This study reveals the sensory trait-related chemical changes in the yellowing process of tea, which provides a theoretical basis for the optimization of the yellowing process and quality control of yellow tea.

Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm

In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R²) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R² values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.

Green and white teas as health-promoting foods

Tea is one of the most consumed beverages around the world and as such, it is constantly the object of novel research. This review focuses on the research performed during the last five years to provide an updated view of the current position of tea regarding human health. According to most authors, tea health benefits can be traced back to its bioactive components, mostly phenolic compounds. Among them, catechins are the most abundant. Tea has an important antioxidant capacity and anti-inflammatory properties, which make this beverage (or its extracts) a potential aid in the fight against several chronic diseases. On the other hand, some studies report the possibility of toxic effects and it is advisable to reduce tea consumption, such as in the last trimester of pregnancy. Additionally, new technologies are increasing researchers' possibilities to study the effect of tea on human gut microbiota and even against SARS CoV-2. This beverage favours some beneficial gut microbes, which could have important repercussions due to the influence of gut microbiota on human health.

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches-K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)-were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.